Reliably Assessing Growth with Longitudinal Diagnostic Classification Models

Recent advances have enabled diagnostic classification models (DCMs) to accommodate longitudinal data. These longitudinal DCMs were developed to study how examinees change, or transition, between different attribute mastery statuses over time. This study examines using longitudinal DCMs as an approach to assessing growth and serves three purposes: (1) to define and evaluate two reliability measures to be used in the application of longitudinal DCMs; (2) through simulation, demonstrate that longitudinal DCM growth estimates have increased reliability compared to longitudinal item response theory models; and (3) through an empirical analysis, illustrate the practical and interpretive benefits of longitudinal DCMs. A discussion describes how longitudinal DCMs can be used as practical and reliable psychometric models when categorical and criterion‐referenced interpretations of growth are desired.     

Interpreting Probabilistic Classifications From Diagnostic Psychometric Models

Although much research has been conducted on the psychometric properties of cognitive diagnostic models, they are only recently being used in operational settings to provide results to examinees and other stakeholders. Using this newer class of models in practice comes with a fresh challenge for diagnostic assessment developers: effectively reporting results and supporting end users to accurately interpret results. Achieving the goal of communicating results in a way that leads users of the assessment to make accurate interpretations requires a prerequisite step that cannot be taken for granted. The assessment developers must first accurately interpret results from a psychometric, or measurement, standpoint. Through this article, we seek to begin a discussion about reasonable interpretations of the results that classification‐based models provide about examinees. Interpretations from published research and ongoing practice show different—and sometimes conflicting—ways to interpret these results. This article seeks to formalize a comparison, critique, and discussion among the interpretations. Before beginning this discussion, we first present background on the results provided by classification‐based models regarding the examinees. We then structure our discussion around key questions an assessment development team needs to answer themselves prior to constructing reports and interpretative guides for end users of the assessment.     

化学学习困难的诊断性评价分析

在回顾学习困难的研究历史、梳理分析化学学习困难研究成果的基础上,以诊断性评价为指导,从知识的理解、知识的应用以及学习策略三个方面入手,系统地探索了化学学习困难的成因及类型,并对中学化学教学提出了一些针对性建议。     

认知诊断评价及其关键技术

认知诊断测验是近年来兴起的测验形式,受到越来越多研究人员的关注。认知诊断测验与传统的纸笔测验、计算机自适应测验最大的不同之处在于它能够提供被试者在知识上的详细掌握情况,能够让被试者了解自身的长处和不足,可以进行有针对性的补救学习;能够让指导者了解学生在知识上的优点和弱点,可以进行有针对性的补救教学。认知诊断测验是适应现代教育的发展而产生的,认知诊断测验的功能虽然强大,但是要真正大规模的实现,却也不是容易的事情,因为实施认知诊断测验涉及很多关键技术,并且有的技术目前还不成熟。对实施认知诊断测验的关键技术进行系统性的介绍,以使更多的研究人员投身到诊断测验的研究中来,促进认知诊断测验的发展。     

Obtaining Diagnostic Classification Model Estimates Using Mplus

Diagnostic classification models (aka cognitive or skills diagnosis models) have shown great promise for evaluating mastery on a multidimensional profile of skills as assessed through examinee responses, but continued development and application of these models has been hindered by a lack of readily available software. In this article we demonstrate how diagnostic classification models may be estimated as confirmatory latent class models using Mplus, thus bridging the gap between the technical presentation of these models and their practical use for assessment in research and applied settings. Using a sample English test of three grammatical skills, we describe how diagnostic classification models can be phrased as latent class models within Mplus and how to obtain the syntax and output needed for estimation and interpretation of the model parameters. We also have written a freely available SAS program that can be used to automatically generate the Mplus syntax. We hope this work will ultimately result in greater access to diagnostic classification models throughout the testing community, from researchers to practitioners.     

Knowledge Integration in Science Learning: Tracking Students' Knowledge Development and Skill Acquisition with Cognitive Diagnosis Models

In scientific literacy, knowledge integration (KI) is a scaffolding-based theory to assist students' scientific inquiry learning. To drive students to be self-directed, many courses have been developed based on KI framework. However, few efforts have been made to evaluate the outcome of students' learning under KI instruction. Moreover, finer-grained information has been pursued to better understand students' learning and how it progresses over time. In this article, a normative procedure of building and choosing cognitive diagnosis models (CDMs) and attribute hierarchies was formulated under KI theory. We examined the utility of CDMs for evaluating students' knowledge status in KI learning. The results of the data analysis confirmed an intuitive assumption of the hierarchical structure of KI components. Furthermore, analysis of pre- and posttests using a higher-order, hidden Markov model tracked students' skill acquisition while integrating knowledge. Results showed that students make significant progress after using the web-based inquiry science environment (WISE) platform.     

学习困难鉴定模型的新发展

能力-成绩差异模型是使用得最广泛的学习困难鉴定方法,但近年来,学者们从理论假设和实践应用上对该模型进行了猛烈的批评,并提出了新的鉴定模型:反应-干预模型。通过研究证明它比前者具有良好的信效度。尽管对反应-干预模型还存在争论,但其提倡的早期和综合性鉴别、诊断与干预治疗相结合等思想将指导今后学习困难的研究。     

基于诊断性动态测评的英语写作教学模式运用与反馈

诊断性测评是一种语言测评形式,在语言教学中具有很高的反馈价值,而动态测评则是强调测评过程与要素的完全整合。在英语写作教学中,教学反馈的有效性直接影响着教学效果。因此将诊断性动态测评应用于英语写作教学模式中,通过一系列从隐性到显性的介入策略,加之及时有效的诊断性信息反馈,可切实提升二语习得的质量与效率。     

一种针对道路潜在安全隐患的跟踪学习方法

提出了一种对存有安全隐患车辆的跟踪学习方法,对传统的在线学习方法进行改进,通过改善学习性能,使算法在不稳定的安全隐患车辆跟踪中发挥作用,实验结果表明,在同等条件下,该算法能够获取更多有效样本,使得分类器更快收敛。     

跟踪误差约束下的布菜克-李特曼模型

布莱克-李特曼模型（简称“B—L模型”）在资产资本定价模型的基础上,使用贝叶斯方法把投资者观点加入到隐含期望收益率中。文章对B—L模型做了深入的分析．并且把模型应用到单个股票中。在这里我们用跟踪误差约束下的优化方法,找到了优化模型最佳跟踪误差。然后参照此跟踪误差确定了B—L模型的跟踪误差的大小,从而也就给出了B—L模型其他参数的数值,这减少了参数设定的随意性,使得模型更具有操作性和统一性。     

诊断性语言测评视角下大学英语写作课堂教学改革模式探究

测评是写作课堂实施过程中的一个必要环节,通过定期“诊断性”的语言测评,教师能够及时了解学生对知识点的掌握和运用情况,发现他们在写作中遇到的问题,并及时给出针对性的指导意见,真正做到答疑解惑。在诊断性语言测评视角下探索写作教学新模式,结合网络平台实现大规模、高频率的写作目标任务,“诊断”学生阶段性的写作现状,实现评教衔接、以评促教、以评促学,从而达到更好的教学效果和学习效果,真正培养学生的写作能力。     

Longitudinal Evaluation of a Scale-Up Model for Teaching Mathematics With Trajectories and Technologies

Abstract

We used a cluster randomized trial to evaluate the effectiveness of a research-based model for scaling up educational interventions, focusing on the persistence of effects with and without a follow-through intervention. The instantiation of the Technology-enhanced, Research-based, Instruction, Assessment, and professional Development (TRIAD) model emphasized teaching early mathematics for understanding via learning trajectories and technology. The TRIAD implementation began in 42 schools in two city districts serving low-resource communities, randomly assigned to three conditions. In pre-kindergarten, the 2 experimental interventions were identical, but 1 included follow-through in the kindergarten year, including knowledge of the pre-K intervention and ways to build upon that knowledge using learning trajectories. Intent-to-treat analyses showed that students in both the follow-through condition (g = .33) and non-follow-through condition (g = .22) scored statistically significantly higher than children in the control condition. Both groups outperformed the control condition in treatment-on-the-treated analyses (g = .38, follow-through; g = .30 non-follow-through). Moderators and mediators were also analyzed. We conclude that the instantiation of the TRIAD model was successful and that follow through may contribute to the persistence of the effects of preschool interventions.     

核心素养的认知诊断测评体系建构

核心素养作为人的必备品质和关键能力,其测评成为了教育教学研究的主要议题。认知诊断理论与素养测评具有内在一致性,在分析认知诊断测评核心素养的合理性和可行性的基础上,借助认知诊断测评理论,文章从核心素养的基本属性关系构建、认知诊断测试的编制、认知诊断模型的选择与应用、测评结果解释与诊断报告的制定等方面建构起素养测评的完整体系,并以数感为例对测评体系的应用做了解析。结合当前核心素养测评的现状,文章针对认知诊断的核心素养测评给出了启示,为核心素养的测评提供了新的视角。     

远程教学平台中"交互及协作学习支持"模型建构研究

教学平台是远程教育中教与学重新整合的纽带，但调研发现许多平台在教学功能和交互等方面存在一些不足。本文根据远程教育交互及协作学习的理论，建立了用于改进平台功能的“交互及协作学习支持”模型。该模型包括面向对象和教学内容增值的网络课程设计模块、开放互助式协作学习模块、即时通讯和手机短信系统嵌入模块、学生行为跟踪及教师导学模块等五个部分，文章还对每个模块的功能、内部结构和实现技术作了详细的阐述。     

网络学习中反思性学习及模型研究

为实现网络学习中的有效学习,文章在了解国内外反思性学习研究的基础上,从哲学的角度对反思及反思性学习做了重新认识:反思是一个过程,更是一种能力,是支撑整个反思迭代循环过程的能力集合体;反思性学习是一种内在内容不变,外在形式随时代不断变迁的传统学习策略。其次,以班杜拉三元交互决定论为理论基础,通过对网络学习与传统课堂学习进行比较分析,对影响网络学习中反思性学习的因素做了初步探究,并尝试构建了网络学习中反思性学习模型。     

纵向数据半参数模型估计的相合性

考虑纵向数据半参数回归模型:Yij=XiTjβ+g(Tij)+iεj.基于最小二乘法和局部线性拟合的方法建立了模型中参数分量β,回归函数g(.)和误差方差σ2的估计,并在适当条件下证明了估计量的相合性.     

Modeling Growth in Electronic Learning Environments Using a Longitudinal Random Item Response Model

In educational environments, monitoring persons' progress over time may help teachers to evaluate the effectiveness of their teaching procedures. Electronic learning environments are increasingly being used as part of formal education and resulting datasets can be used to understand and to improve the environment. This study presents longitudinal models based on the item response theory (IRT) for measuring persons' ability within and between study sessions in data from web-based learning environments. Two empirical examples are used to illustrate the presented models. Results show that by incorporating time spent within- and between-study sessions into an IRT model; one is able to track changes in ability of a population of persons or for groups of persons at any time of the learning process.     

成人自主学习的新模型

自主学习是一种学习者本人进行的自我设计、自我评价和自我分析的学习。关于如何促进成人学习者自主学习,该文针对它的教授过程,提供了一个三阶段模型:向学习者说明强化学习的思维、感知及反思方式;向学习者讲述自主思维、感知及反思的过程;安排让学习者参与自主学习的活动。     

获得学习模型的困境与参与学习模型的转向

根据基础信念的不同,可将学习的研究与实践分为获得与参与两种取向的模型。获得模型假定学习即获得某种实体材料的过程,参与模型假定学习即成为团体成员的过程。20世纪末期学习理论发生由获得向参与转向的原因在于,获得模型面临的认识论困境以及所产生的不良规范与价值问题。要恰当处理获得模型与参与模型的关系,须正确认识知识的迁移,不偏废一方,使二者有机互补,并根据实际需要选择与应用某个模型。     

远程学习策略模型的建构研究

学习策略是衡量个体学习能力的重要尺度,也是制约学习效果的重要因素。受传统学习模式和远程学习环境的影响,在当前的远程教育中,远程学习者缺乏对学习策略的理解和有效运用。为此,应对以在职成人为主的远程学习者进行特征性分析,并将远程学习活动与学习策略相结合,构建有针对性和实效性的远程策略模型,以解决远程学习者低起点、低动力、低投入和低策略的问题。